Malware (viruses, trojans, “advanced persistent threats,” etc.) represents a significant potential risk in embedded network systems, such as, for example, computer networks in factory control systems. Safeguarding the integrity of a given network is often an important task for ensuring the overall safety of critical systems. As a result, detection of viruses and malware is an increasingly critical task in embedded systems.
Unfortunately, recent trends demonstrate that malware creators are willing to dedicate significant time and resources to the dissemination of malware, and the malware can often be cloaked and hidden in sophisticated ways. Usefully, viruses and hosts have been waging an on-going war in the biological domain for many centuries. The outcome of the biological war has been a remarkably sophisticated and subtle system that can quickly detect, attack, and kill harmful invaders, while managing to avoid not only damage to the self, but also killing other symbiotic organisms in the body.
Artificial immune systems (AIS) are a collection of algorithms developed from models or abstractions of the function of the cells of the human immune ne system. One category of AIS is based on the Danger Theory; and includes the Dendritic Cell Algorithm (DCA), which is based on the behavior of Dendritic Cells (DCs) within the human immune system. DCs have the power to suppress or activate the immune system through the correlation of signals from an environment, combined with location markers in the form of antigen. The function of a DC is to instruct the immune system to act when the body is under attack, policing the tissue for potential sources of damage. DCs are natural anomaly detectors, the sentinel cells of the immune system. The DCA has demonstrated potential as a static classifier for a machine learning data set and anomaly detector for real-time port scan detection.
The DCA has been described in a number of references, including Greensmith, Aickelin and Twycross, Articulation and Clarification of the Dendritic Cell Algorithm. In Proc. of the 5th International Conference on Artificial Immune Systems, LNCS 4163, 2006, pp. 404-417. The following features of the DCA differentiate the algorithm from other MS algorithms: (1) multiple signals are combined and are a representation of environment or context information; (2) signals are combined with antigen in a temporal and distributed manner; (3) pattern matching is not used to perform detection, unlike negative selection; and (4) cells of the innate immune system are used as inspiration, not the adaptive immune cells, and unlike clonal selection, no dynamic learning is attempted.
As described in the DCA literature, DCs can perform various functions, depending on their state of maturation. Modulation between these maturation states is facilitated by the detection of signals within the tissue, namely: (1) danger signals, (2) pathogenic associated molecular patterns (PAMPs), (3) apoptotic signals (safe signals), and (4) inflammatory cytokines. The DCA has been implemented successfully in various localized applications, which have made use of danger signals, PAMPs, and safe signals. However, existing DCA implementations have not made use of signals analogous to the inflammatory cytokines of DCs in the biological domain.